Add heatmap (closes #64)

examples/analysis.py

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+from progpy.analysis import show_heatmap
+from progpy.datasets import nasa_cmapss
+
+(training, testing, eol) = nasa_cmapss.load_data(1)
+
+show_heatmap(training)
+
+# Notice that some values have no color- this is because they are constant. Let's drop these
+for feature in ['setting3', 'sensor1', 'sensor5', 'sensor10', 'sensor16', 'sensor18', 'sensor19']:
+    training.drop(feature, axis=1)
+show_heatmap(training)
+
+# Here you can see high correlations between sensor 14 and 9

src/progpy/analysis/__init__.py

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+# Copyright © 2021 United States Government as represented by the Administrator of the
+# National Aeronautics and Space Administration.  All Rights Reserved.
+
+from progpy.analysis.heatmap import show_heatmap

src/progpy/analysis/heatmap.py

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+# Copyright © 2021 United States Government as represented by the Administrator of the
+# National Aeronautics and Space Administration.  All Rights Reserved.
+
+import matplotlib.pyplot as plt
+
+def show_heatmap(data):
+    """
+    Generate a heatmap showing correlation between parameters.
+
+    Code from: https://github.com/keras-team/keras-io/blob/13d513d7375656a14698ba4827ebbb4177efcf43/examples/timeseries/timeseries_weather_forecasting.py#L152
+
+    Args:
+        data (np.ndarray): Array of data where each column is a variable.
+    """
+    plt.matshow(data.corr())
+    plt.xticks(range(data.shape[1]), data.columns, fontsize=14, rotation=90)
+    plt.gca().xaxis.tick_bottom()
+    plt.yticks(range(data.shape[1]), data.columns, fontsize=14)
+
+    cb = plt.colorbar()
+    cb.ax.tick_params(labelsize=14)
+    plt.title("Feature Correlation Heatmap", fontsize=14)
+    plt.show()